library(rbioacc)
# library(ggplot2)

A Simple Example: Male Gammarus Single

Data

Load the data set with the function data(), define the duration of the exposure time_accumulation, and check if the data set is correctly imported with the function modelData(). Here the data set is called Male Gammarus Single

data("Male_Gammarus_Single")

Inference

The function fitTK() performs the inference process.

modelData_MGS <- modelData(Male_Gammarus_Single, time_accumulation = 4)
fit_MGS <- fitTK(modelData_MGS, iter = 10000)

Results

TK parameters

The 4 MCMC are stored in the object fitMCMC. The quantiles for each TK parameter can be obtained with the quantile() function.

quantile_table(fit_MGS)
#>                   2.5%          50%        97.5% parameter
#> ku        552.34816720 615.50212256 680.59347562        ku
#> kee         0.02305822   0.03387778   0.04523326       kee
#> sigmaConc   0.01086810   0.01460538   0.02104300 sigmaConc

plot(fit_MGS)

ppc(fit_MGS)

Male Gammarus Merged

data("Male_Gammarus_Merged")
data_MGM708 <- Male_Gammarus_Merged[Male_Gammarus_Merged$expw == 7.08021e-05, ]
modelData_MGM708 <- modelData(data_MGM708, time_accumulation = 4)
fit_MGM708 <- fitTK(modelData_MGM708, iter = 10000)

quantile_table(fit_MGM708)

plot(fit_MGM708)

ppc(fit_MGM708)

data_MGM141 <- Male_Gammarus_Merged[Male_Gammarus_Merged$expw == 1.41604e-04, ]
modelData_MGM141 <- modelData(data_MGM141, time_accumulation = 7)
fit_MGM141 <- fitTK(modelData_MGM141, iter = 20000)

quantile_table(fit_MGM141)

plot(fit_MGM141)

ppc(fit_MGM141)

data_MGM283 <- Male_Gammarus_Merged[Male_Gammarus_Merged$expw == 2.83208e-04, ]
modelData_MGM283 <- modelData(data_MGM283, time_accumulation = 4)
fit_MGM283 <- fitTK(modelData_MGM283, iter = 10000)

quantile_table(fit_MGM283)

plot(fit_MGM283)

ppc(fit_MGM283)

Male Gammarus seanine with growth

data("Male_Gammarus_seanine_growth")
modelData_MGSG <- modelData(Male_Gammarus_seanine_growth, time_accumulation = 1.417)
fit_MGSG <- fitTK(modelData_MGSG, iter = 10000)

quantile_table(fit_MGSG)

plot(fit_MGSG)

ppc(fit_MGSG)

Oncorhynchus

data("Oncorhynchus_two") #  Pimephales_two
data_OT440 = Oncorhynchus_two[Oncorhynchus_two$expw == 0.00440,]
modelData_OT440 <- modelData(data_OT440, time_accumulation = 49)
fit_OT440 <- fitTK(modelData_OT440, iter = 10000)

quantile_table(fit_OT440)

plot(fit_OT440)

ppc(fit_OT440)

data_OT041 <- Oncorhynchus_two[Oncorhynchus_two$expw == 0.00041,]
modelData_OT041 <- modelData(data_OT041, time_accumulation = 49)
fit_OT041 <- fitTK(modelData_OT041, iter = 10000)

quantile_table(fit_OT041)

plot(fit_OT041)

ppc(fit_OT041)

Chironomus benzo-a-pyrene

data("Chironomus_benzoapyrene")
modelData_CB <- modelData(Chironomus_benzoapyrene, time_accumulation = 3)
modelData_CB$unifMax = modelData_CB$unifMax  * 100
fit_CB <- fitTK(modelData_CB, iter = 10000)

quantile_table(fit_CB)

plot(fit_CB)

ppc(fit_CB)

Prediction

data("Male_Gammarus_Single")
modelData_MGS <- modelData(Male_Gammarus_Single, time_accumulation = 4)
fit_MGS <- fitTK(modelData_MGS, iter = 5000, chains = 3)

# Data 4 prediction should respect the exposure routes
data_4pred <- data.frame( time = 1:25, expw = 4e-5 )
predict_MGS <- predict(fit_MGS, data_4pred)
plot(predict_MGS)

# data("Male_Gammarus_seanine_growth")
# modelData_MGSG <- modelData(Male_Gammarus_seanine_growth, time_accumulation = 4)
# fit_MGSG <- fitTK(modelData_MGSG, iter = 5000, chains = 3)
# 
# # Data 4 prediction should respect the exposure routes
# data_4pred <- data.frame( time = 1:25, expw = 18 )
# predict_MGSG <- predict(fit_MGSG, data_4pred)
# plot(predict_MGSG)

data("Chiro_Creuzot")
Chiro_Creuzot <- Chiro_Creuzot[Chiro_Creuzot$replicate == 1,]
modelData_CC <- modelData(Chiro_Creuzot, time_accumulation = 1.0)
fit_CC <- fitTK(modelData_CC, iter = 5000, chains = 3)
# --------
quantile_table(fit_CC)

# Data 4 prediction should respect the exposure routes
data_4pred <- data.frame( time = 1:25, expw = 18, exps = 1200, exppw = 15 )
predict_CC <- predict(fit_CC, data_4pred)
plot(predict_CC)

Tutorial